BS/MS in Computer Engineering, Electrical Engineering, Computer Science, or related field, or equivalent experience.
Strong C/C++ development skills for embedded, real-time systems with a focus on resource constraints.
Experience in porting and optimizing signal-processing or machine-learning algorithms for embedded platforms.
Proficient in profiling and optimizing embedded software for performance and efficiency.
Strong systems thinking and collaboration skills across multiple engineering teams.
Familiarity with embedded optimization techniques like DSP optimization and model compression.
Experience with physiological sensing or wearable devices is a plus.
Responsibilities
Own the technical path for porting Sensor Intelligence algorithms to production on WHOOP devices.
Translate algorithms from Python and MATLAB into efficient C/C++ for embedded systems.
Resolve dependencies for on-device execution across various platforms and services.
Integrate algorithms with firmware and sensor infrastructure, defining interfaces and requirements.
Establish equivalence between reference and edge implementations, profiling performance and debugging issues.
Build tools and infrastructure to streamline future algorithm migrations across WHOOP hardware.
Benefits
Opportunity to work on cutting-edge wearable technology.
Collaborative environment with cross-functional teams.
Focus on innovation and algorithm optimization.
Commitment to diversity and inclusion in the workplace.
Encouragement to apply even if not all qualifications are met.
Full Job Description
WHOOP is hiring an Edge Algorithm Integration Engineer to join the Edge ML team and bring Sensor Intelligence algorithms from validated reference implementations to production execution on WHOOP devices. You will own the technical path from reference algorithm to edge deployment, translating and optimizing algorithms for embedded hardware, resolving cross-platform dependencies, integrating with firmware and sensor infrastructure, and ensuring implementations preserve expected algorithm performance. You will work across Sensor Intelligence, Firmware, Edge ML Platform, Mobile, Cloud, and Connectivity teams to enable algorithms to run reliably and efficiently within the compute, memory, latency, and power constraints of WHOOP devices.
RESPONSIBILITIES:
Own the end-to-end technical path for porting developed and validated Sensor Intelligence algorithms from reference environments to production-ready implementations on WHOOP embedded platforms.
Translate Python, MATLAB, and other reference implementations into efficient, production-quality C/C++, optimizing signal-processing and machine-learning algorithms for compute, memory, latency, power, and real-time execution.
Understand algorithm architecture, data flow, runtime requirements, and dependencies across cloud, mobile, connectivity, sensor pipelines, libraries, and platform services; drive the technical work required to resolve or redesign dependencies for on-device execution.
Integrate algorithms with firmware, sensor pipelines, embedded services, and Edge ML platform capabilities, partnering closely with Sensor Intelligence and Firmware teams to define interfaces, requirements, acceptance criteria, and system-level behavior.
Establish functional and numerical equivalence between reference and edge implementations, profile on-device performance, and debug complex issues spanning algorithms, sensors, firmware, and embedded systems.
Build reusable tools, test harnesses, profiling infrastructure, and deployment patterns that accelerate future algorithm migrations and enable efficient portability across current and next-generation WHOOP hardware platforms.
QUALIFICATIONS:
BS/MS in Computer Engineering, Electrical Engineering, Computer Science, or a related technical field, or equivalent practical experience.
Strong C/C++ software development experience with embedded, real-time, or resource-constrained systems, including an understanding of memory management, compute limitations, latency, and power constraints.
Experience porting, integrating, or optimizing signal-processing or machine-learning algorithms for MCU-based, embedded, or edge platforms, including translating implementations from Python, MATLAB, or similar reference environments.
Experience profiling and optimizing embedded software for runtime, memory footprint, computational efficiency, and/or power, with strong debugging skills across algorithm and systems boundaries.
Strong systems thinking and software integration skills, with the ability to understand complex dependencies and collaborate across algorithm, firmware, platform, and other engineering teams to drive technical issues to resolution.
Experience with one or more relevant embedded optimization technologies or techniques, such as DSP optimization, fixed-point implementation, quantization, model compression, ARM Cortex-M, DSPs, NPUs, RTOS environments, CMSIS-DSP/CMSIS-NN, or TensorFlow Lite Micro.
Experience with physiological sensing, wearable devices, time-series sensor data, low-power systems, or establishing functional and numerical equivalence between reference and embedded implementations is a plus.
Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions.
Interested in the role, but don't meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.
About WHOOP
WHOOP is a wearable technology company that specializes in fitness tracking. The company was founded in 2012 and is based in Boston, Massachusetts. WHOOP's flagship product is a wristband that tracks various metrics related to fitness and health, such as heart rate variability, sleep quality, and recovery time. The company also offers a subscription service that provides personalized insights and recommendations based on the data collected by the wristband. WHOOP has raised over $200 million in funding and has partnerships with several professional sports leagues and teams.
